Fingerprint recognition system

ABSTRACT

A method of analyzing and recognizing fingerprint images that utilizes vector processing of a vector field that is defined as the tangential vector of the fingerprint ridge curves is disclosed. The raw fingerprint image is divided into blocks, filtered to remove noise, and the orientation direction of each block is found. This allows the ridge curves to be enhanced and approximated by piece-wise linear approximations. The piece-wise linear approximations to the ridge curves allow the minutiae to be extracted and classified and a fingerprint minutiae template to be constructed. An enrollment process gathers multiple fingerprint images, creates fingerprint minutiae templates corresponding to the acquired fingerprint images, and stores the templates and other data associated with the respective individual or the enrolled fingerprint in a fingerprint database. In an identification process, an unknown raw fingerprint image is obtained via a fingerprint scanner and processed similarly to the enrollment process such that the fingerprint minutiae template of the unknown fingerprint is compared to one or more previously enrolled fingerprint minutiae templates. The identity of the individual associated with the unknown fingerprint is thereby ascertained. In addition, live finger detection can be accomplished in conjunction with the verification or identification process through analysis of the fingerprint image thus enhancing the security of the overall system.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Ser. No. 10/156,447 filed May 28,2002 which claims priority under 35 U.S.C. §119(e) to U.S. ProvisionalPatent Application Ser. No. 60/293,487 filed May 25, 2001 and U.S.Provisional Patent Application Ser. No. 60/338,949 filed Oct. 22, 2001.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

N/A

BACKGROUND OF THE INVENTION

A biometric is defined as a biological characteristic or trait that isunique to an individual and that can be accurately measured. A biometricthat can be stored and accessed in an efficient manner can be used toidentify an individual or to verify the identity of an individual. Abiometric commonly used to identify human beings is one or morefingerprints belonging to the particular human being.

Fingerprint identification of a human being consists of two stages:enrollment and verification/identification. Enrollment of a fingerprintinvolves taking a fingerprint image (FPI) of an individual and storingthe FPI itself or a plurality of data that is representative of the FPIin an FPI database. Identification of a fingerprint involves taking anFPI of an unknown individual and comparing the unknown FPI to the FPIsor FPI data that is stored in the FPI database. An identification ismade when a match between the unknown FPI and an FPI stored in the FPIdatabase is found that has a sufficient reliability that the probabilityof a false positive is below a predetermined threshold. Fingerprintverification or authentication matches an individual to a fingerprintthat has been previously enrolled by that individual. Thus,identification involves searching for a match between a single unknownFPI with many stored FPIs. The verification process involves thematching an unknown or unconfirmed fingerprint minutiae template to asingle previously enrolled fingerprint minutia template. Accordingly,the verification process is a one-to-one matching technique.

The use of biometrics to restrict access to secure entities such ascomputer networks, cryptographic keys, sensitive data, and physicallocations is well known. In addition, smart cards, cards that have abiometric, such as a fingerprint, encoded thereon can be used to providetransaction security as well. A smart card allows a user to provide thebiometric encoded on the card, wherein the encoded biometric data iscompared to the biometric measured on the individual. In this way, asmartcard can positively authenticate the identity of the smartcarduser.

However, traditional FPI data is based on the set of singularities thatcan be classified according the type of singularity, e.g., deltas,arches, or whorls. In addition, FPIs contain fingerprint minutiae thatare the end point of a ridge curve or a bifurcation point of a ridgecurve. FPI images can be classified and matched according to dataassociated with the fingerprint minutiae. This data can include theposition of the minutiae, the tangential direction of the minutiae, andthe distance to other minutiae. These types of FPI data can lead to ahigh false acceptance or identification rate when the unknown FPI hasonly a few minutiae or if the unknown FPI is only a partial FPI that mayor may not include the number of minutiae needed to accurately verify oridentify the unknown FPI.

Therefore what is needed is a method and apparatus to collect, analyze,and store FPI data such that an unknown or unverified FPI can beaccurately verified or identified in the FPI or whether the FPI is onlya partial print.

BRIEF SUMMARY OF THE INVENTION

A method of analyzing and recognizing fingerprint images that utilizesvector processing of a vector field that is defined as the tangentialvector of the fingerprint ridge curves is disclosed. The raw fingerprintimage is divided into blocks, each block is filtered to remove noise andthe orientation direction of each block is found. This allows the ridgecurves to be enhanced and approximated by piece-wise linearapproximations. The piece-wise linear approximations to the ridge curvesallow the minutiae to be extracted and classified and a fingerprintminutiae template to be constructed. An enrollment process gathersmultiple fingerprint images, creates fingerprint minutiae templatescorresponding to the fingerprint images, and stores the templates andother data associated with the respective individual or the enrolledfingerprint in a fingerprint database. In an identification orverification process an unknown raw fingerprint image is obtained via afingerprint scanner and processed similarly to the enrollment processdescribed above. The fingerprint minutiae template of the unknownfingerprint is compared to one or more previously enrolled fingerprintminutiae templates to identify or verify the identity of the individualassociated with the unknown fingerprint. In addition, live fingerdetection can be accomplished in conjunction with the identification orverification processes through analysis of the fingerprint image thusenhancing the security of the overall system.

Other forms, features, and aspects of the above-described methods andsystem are described in the detailed description that follows.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

The invention will be more fully understood from the following detaileddescription taken in conjunction with the accompanying drawings inwhich:

FIG. 1 is a flow chart of a method for acquiring and enrollingfingerprint minutiae templates;

FIG. 2 is a flow chart of a method for extracting minutiae from a rawfingerprint image and forming a fingerprint minutiae template;

FIG. 3 is a schematic diagram of a direction filter suitable for use inthe present fingerprint analysis method;

FIG. 4 is a flow chart of a method for identifying/verifying theidentity of an individual using the presently described fingerprintanalysis method;

FIG. 5 is a flow chart of a method for comparing an unknown fingerprintminutiae template with a previously enrolled fingerprint minutiaetemplate;

FIG. 6 is block diagram for a system to control physical access usingthe fingerprint analysis methods described herein;

FIG. 7 is a block diagram for a system to control computer networkaccess using the fingerprint analysis methods described herein;

FIG. 8 is a block diagram for a system to control access to a web pageacross the internet using the fingerprint analysis methods describedherein;

FIG. 9 is a flow chart for a method of using the presently describedfingerprint analysis methods in conjunction with a smartcard;

FIG. 10 is a flow chart for a method of detecting a live finger byanalyzing the binary fingerprint image;

FIG. 11 is a flow chart for a method of detecting and classifyingsingularities found in the finger print image; and

FIG. 12 is a flow chart for a method of estimating the resolution of araw fingerprint image.

DETAILED DESCRIPTION OF THE INVENTION

A fingerprint image (FPI) acquisition, analysis, storage, andrecognition system is disclosed in which FPIs are acquired and afingerprint template based upon the acquired FPI is created. Thefingerprint template is stored and can be used to both identify anunknown FPI and to verify the identity of an FPI.

FIG. 1 is block diagram of the enrollment process used to acquire an FPIand to store the corresponding fingerprint template. In particular, theraw FPI is acquired from a fingerprint sensor or scanner or a scannedFPI, as depicted in step 102. As used herein a raw FPI is defined as anoriginal fingerprint image captured by a fingerprint sensor or scanneror a raw fingerprint can be a digitally scanned image of a paper and inkfingerprint. A raw FPI includes a plurality of ridge curves and valleysinterspersed between the various ridge curves corresponding to theridges and valleys of the original fingerprint. The ridge curves andvalleys form various structures that include singularities such aswhorls, deltas, arches, and also include fingerprint minutiae that arethe ending point of ridge curves or bifurcation points of ridge curves.Each of the minutiae has data associated therewith that is indicative ofthe position of the minutiae, the tangential direction of the minutiae,and the type of minutiae.

The raw FPI is processed to enhance the contrast between the ridgecurves and valleys contained in the FPI, as depicted in step 104. Asdepicted in step 106, the quality of the enhanced FPI is evaluated andif the quality of the FPI is sufficiently high, the minutiae from theFPI are extracted and control is passed to step 108. If not, controlpasses to step 102 and another FPI is acquired. As depicted in step 108,the number of minutia are examined and if there are sufficient minutiae,control is passed to step 110 where the minutiae are extracted from theFPI and an FPI template is formed. In general the number of minutiaethat are required is dependent upon the level of security that isrequired. A low security application may only require six minutiae thatare able to be matched, while a high security application may require 12or more minutiae that are able to be matched.

As used herein a fingerprint template is an undirected graph of minutiaeextracted from an FPI. Each node in the fingerprint template is anindividual minutia and each connecting segment in the graph connects twominutiae (i.e., graph nodes). Each connecting segment also includes dataassociated therewith, for example, cross points of the connectingsegment with ridge curves, and the angles between the direction of theconnecting segment and the tangential direction of the ridge curve atthe intersecting point. In addition, the template can include data onthe core and deltas associated with the FPI. For example, the FPItemplate can include data associated with a core or delta such as theposition and direction of respective core and delta.

The fingerprint template is associated with the individual and thenstored in a fingerprint template database, as depicted in step 112. Ifthere are a not sufficient number of minutiae, control passes to step102 and another RAW FPI is acquired.

FIG. 2 is a flowchart that describes the various steps necessary toperform the image processing of the raw FPI, the minutiae extraction,and the FPI template formation. The steps depicted in FIG. 2 can be usedto process raw FPIs for enrollment purposes, and raw FPIs foridentification or identity verification purposes.

As depicted in FIG. 2, a raw FPI is acquired from a fingerprint scanneror from scanning a paper and ink fingerprint, or from a previouslydigitized FPI, as depicted in step 202. The raw FPI is separated into anarray of non-overlapping blocks, as depicted in step 204. The block sizecan be selected based upon various parameters such as the size of theFPI, the amount of data contained therein, and the processor speed.Preferably, the block size is selected as a function of the resolutionof the FPI such that within each block, the ridge curves can beapproximated by straight lines. In one preferred embodiment, the blocksize is given by R/25 and rounded to the closest power of 2, where R isthe resolution of the FPI in dots/inch. In the illustrated embodiment,the resolution of a typical fingerprint scanner is approximately 500 dpiand is divided into 256 blocks in a 16×16 block pattern of equal sizeblocks. In another embodiment, the block size may be varied within anFPI depending upon the size of the object within the FPI that is to beprocessed.

The blocked image is processed to provide one or more regions ofinterest, as depicted in step 206. A region of interest in the FPI is aportion or portions of the FPI containing the ridge curves and valleysof the FPI, the remaining portion or portions of the FPI do not containany significant fingerprint data. To determine the regions of interest,the FPI is separated into foreground blocks and background blocks, asdepicted in step 206. In one embodiment, the mean and variance of thepixel intensities are determined for each block. A predetermined meanthreshold and variance threshold are selected and a k-nearest neighborclustering algorithm is used to classify all blocks within the K-nearestneighbors as a foreground block or a background block. In a preferredembodiment, a convex hull is formed that includes all of the blocksdetermined to be foreground blocks. A second check of all backgroundblocks is made to ensure that noise or other interference has notinadvertently switched a foreground block into a background block. Acheck is made to determine if the center of a previously determinedbackground block is contained within the convex hull formed by theforeground blocks. If so, the background block is converted into aforeground block.

The regions of interest in the FPI are filtered to remove random noisein order to form a clearer filtered mage, as depicted in step 208.Random noise is typically high frequency noise and accordingly a lowpass filter is used to smooth out the high frequency noise from theforeground blocks of the blocked image. In one embodiment, the low passfilter is a Gaussian filter. The Gaussian filter can be a 2-dimensionalfilter mask that when convolved with each pixel, within each of theforeground blocks, removes the high frequency noise contained within theFPI.

The orientation angle and magnitude of each of the foreground blocks inthe filtered image are found, forming an orientation image, as depictedin step 210. In general, the orientation angle and magnitude are foundby determining the gradient in the x and y directions. In oneembodiment, a Sobel differential operator is applied to each foregroundblock to determine the orientation angle and amplitude. In the eventthat the orientation amplitude is below a predetermined threshold, aHough transformation is used to estimate the orientation angle.

The contrast between the ridge curves and the valleys in the orientationimage is increased forming a ridge-enhanced FPI, as depicted in step212. In particular, a plurality of directional filters eachcorresponding to a foreground block smoothes out the differences alongthe ridge curves and intensifies the contrast between the ridge curvesand valleys within the corresponding block. In one embodiment, thedirectional filter is a 2-dimensional mask having an x and y direction.The y direction of the mask is intended to amplify the fingerprint ridgecurves and to negatively amplify the valleys. In one embodiment, thedirectional filter is a Gaussian filter along the ridge direction.

A directional filter mask is depicted in FIG. 3 in which the filter mask300 is a square in which the side length is equal to the period of thesignal, or the period of the signal plus 1, whichever is an odd number.The middle rows 302 are selected to enhance the ridges, and the siderows 310 are used to negatively amplify the valleys. There may betransition rows 308 between the middle rows 302 and the side rows 308that have coefficients equal to zero. The center coefficient, a₀, 305 ofthe center row 304 is set to a₀ and the coefficients of the center row304 are cosine tapered to edge values of a₀/4 forming a symmetric row.In the illustrated embodiment, a₀ is set to 1000 and the center row 304is cosine tapered to a value of 250 at each edge. The coefficients ofthe middle rows 302 are cosine tapered from the value of the center rowto a value of a_(0,i)/1.41, where a_(0,i) is the value of the i^(th)coefficient of the center row 304. The value of each coefficient of theside rows 310 is given by$b_{i} = {{- \frac{1}{2}}\left( {\sum\limits_{j = 1}^{m}\quad a_{i,j}} \right)}$where i is the i^(th) coefficient of the side row and m is the number ofmiddle rows. Once the directional filter mask for a block has beendetermined, the directional filter mask is convolved with the pixels inthe corresponding block.

The ridges and valleys of the ridge-enhanced FPI are then separated intoone of two binary values, a first binary value for a ridge pixel and asecond binary value for a valley pixel, forming a binary fingerprintimage, as depicted in step 214. In particular, the image binarization isaccomplished by establishing a binary threshold and comparing theintensity value of each pixel to the binary threshold. A pixel having apixel value greater than the binary threshold is set to a first valueand a pixel having a pixel value less than the binary threshold is setto a second value. In one embodiment in which the maximum pixelintensity is 255, the binary threshold is one-half the maximum pixelintensity or 128. The first value is equal to 255 and the second valueis equal to zero.

The ridge curves and valleys of the binary FPI are thinned to apredetermined width, which in the illustrated embodiment is a singlepixel forming a thinned image, as depicted in step 216. The thinning maybe accomplished with thinning algorithms that are known in the art.

The thinned ridge curves and valleys in the thinned image areapproximated by piece-wise linear segments forming a piece-wise linearFPI, as depicted in step 218. The thinned ridge curves are representedby chain code connecting the start and end points of each ridge curvewithin a corresponding block. A line segment connecting the start andend points of the respective ridge curve is formed and the maximumdistance between the line segment and the ridge curve is determined. Ifthis distance is greater than a predetermined maximum value, two linesegments approximate the ridge curve. A first line segment is formedfrom the start point to the point on the ridge curve having the maximumdistance from the original line segment. A second line segment is formedfrom the end point of the first line segment to the end point of theridge curve. This process is continued iteratively until the distancebetween the ridge curve and any point on the piece wise linearapproximating segments is less than the predetermined minimum value.

The fingerprint minutiae are extracted from the piece-wise linear FPI,as depicted in step 220. In general, minutiae are classified as eitherending minutiae or bifurcation minutiae. Ending minutiae are defined asthe end point of a ridge curve in an FPI and bifurcation minutiae aredefined as a crossing point of two ridge curves in an FPI. Inparticular, a connection number is computed for each pixel in acorresponding block, wherein the connection number is indicative ofwhether a pixel is a fingerprint minutia and if so, what type of minutiathe corresponding pixel is. The connection number is equal to${{C\quad N} = \left( {\sum\limits_{i = 1}^{7}\quad{\frac{1}{2}{{P_{i} - P_{i + 1}}}}} \right)},$

where P_(i) and P_(i+1) are the values of the 8 pixels surrounding thepixel of interest. The connection number corresponds to the propertiesdetailed in Table 1: TABLE 1 Connection number, CN, value Property 0Pixel is an isolated point 1 Pixel is an end point 2 Pixel is acontinuing point 3 Pixel is a branching point 4 Pixel is a crossingpointFor a CN value of 1 or 3, the angle of the ending point or the branchingpoint to the associated ridge curve is determined. The minutiae type,the x-y position of the minutiae, and the angle of the minutiaeassociated with the respective ridge curve are determined and stored.

The extracted minutiae are further processed to remove false minutiaeleaving true minutiae as depicted in step 222. As can be appreciated, alarge number of false minutiae can be created and detected during theprocessing steps prior to this step. These minutiae may be due to smallridge segments, ridge breaks, boundary minutiae, and noise.

For every minutiae extracted in step 220, the minutiae is analyzed tosee if the minutiae belongs to a broken ridge curve, a noisy link, or ifthe extracted minutiae is a boundary minutiae. A broken ridge curveoccurs when two minutiae are within a predetermined distance of oneanother and the directions of the respective minutiae are opposite toone another. If the number of minutiae within a specified area exceeds apredetermined threshold, the minutiae are considered to be part of anoisy link. If minutiae occur along the boundary of the FPI, it isconsidered to be boundary minutiae. In the event that the extractedminutiae belong to one of these three classes, the minutiae is deletedfrom the extracted minutiae list.

A fingerprint minutiae template is then formed from the true minutiae,as depicted in step 224. In particular, a fingerprint minutiae templateis an undirected graph in which the true minutiae are the correspondingnodes and line segments connected between two-node points form the edgesof the graph. Each of the true minutiae is only connected to other trueminutiae within a predetermined distance of it. Data associated with theintersection between a graph edge and any of the ridge curves in the FPIis also stored. This data can include the location of the intersection,i.e., the intersection points, and the angles between the graph edge andtangential direction of the ridge curve.

FIG. 4 depicts a block diagram of an embodiment of theverification/identification process. A raw FPI is acquired from a fingerprint sensor or scanner, as depicted in step 402. The acquired FPI isprocessed, as depicted in step 404, and if the image is suitable forminutiae extraction as depicted in step 406, the number of minutiae thatexist in the FPI is determined, as depicted in step 408. If sufficientminutiae exist in the FPI, the minutiae are extracted and a fingerprintminutiae template is formed as described with respect to FIG. 2, asdepicted in step 410. If the image is not suitable to extract minutiaethen control passes to step 402 and a new raw FPI is acquired.

If the fingerprint minutiae template is formed, one or more of thepreviously enrolled templates are compared to the fingerprint minutiaetemplate of the raw FPI, as depicted in step 412. In the verificationprocess, a single enrolled template that is known a-priori may becompared to the template of the raw FPI in a one to one matching scheme,where the alleged identity of the individual to be verified is known. Inthe identification process, many of the enrolled templates are comparedto the template of the raw FPI in a one to many matching scheme. Asdiscussed in more detail below, the enrolled templates and the templateof the raw FPI may be classified according to various characteristicssuch as the presence of singularities in the FPI to reduce the number ofenrolled fingerprint templates to be searched. The number of minutiaethat are matched is compared to a predetermined threshold, as depictedin step 414, and if the number of matched minutiae exceeds thepredetermined verification threshold, the enrolled template and theunknown/unverified template of the raw FPI are considered matched, asdepicted in step 416. Accordingly, the person is identified or verifiedas the individual associated with the enrolled template. If theindividual associated with the unknown/unverified FPI is cleared forentry into a secure entity such as a computer, a data network, or aphysical space, entry is granted as depicted in step 418. Otherwise,control is passed back to step 402 for acquisition of another FPI.

FIG. 5 depicts an embodiment of a matching process suitable for use withthe identification/verification methods described herein. Havingacquired an enrolled fingerprint template and a fingerprint template tobe identified/verified, first find all node pairs (A,B) that are locallymatched, as depicted in step 502, where A is a minutiae node from theenrolled template and B is a minutiae node from the template to beidentified/verified. For each identified node pair (A,B) atransformation T(B→A) is formed, as depicted in step 504. Thetransformation T(B→A) is defined as the translation of B to A and therotation of B necessary to align B to A. Each node pair (A,B) is furtherused as an anchor node and a neighborhood match is performed in theneighborhood of the anchor node using the corresponding transformationT(B→A), as depicted in step 506. The transformed minutiae nodes in theneighborhood of the node pair (A,B) in each template are compared withone another and if the differences in position and rotation betweencorresponding minutiae are less than a predetermined matching threshold,the minutiae are considered to be matched, as depicted in step 508, 510,and 512. For each node pair (A,B), the number of matched minutiae arecounted, as depicted in step 514. The number of matched minutiae arecompared to a matching threshold, as depicted in step 516. If the numberof matched minutiae exceeds the matching threshold, the fingerprinttemplates are considered to be matched, as depicted in step 518,otherwise, control is returned to step 502.

FIG. 6 depicts a block diagram of a physical access control system 600.A fingerprint scanner 602 is used to scan a fingerprint. The scanned FPIis provided to a fingerprint server 606 that contains fingerprinttemplates of enrolled individuals. The fingerprint server 606 creates afingerprint minutiae template of the scanned FPI and compares thetemplate to the previously enrolled templates corresponding to theindividuals cleared for access to the secure location. A positive matchbetween the fingerprint minutiae template of the scanned FPI and one ofthe previously enrolled fingerprint minutiae templates will positivelyidentify the individual if enrolled. The fingerprint server 606 providesfor match/no-match indicia to be provided to the physical access device604 allowing access into the secured area. Note that the actual identityof the person seeking to gain entrance does not have to be ascertained,although it may be. Only the occurrence of a match between one of thegroup of enrolled fingerprint templates and the fingerprint minutiaetemplate of the scanned FPI is required. However, in a furtherembodiment additional conventional identification establishingtechnologies may be implemented in conjunction with the fingerprintanalysis and identification/verification described herein.

FIG. 7 depicts a block diagram of a network logon control system 700. Afingerprint sensor or scanner 702 coupled to a user PC 704 is used toprovide scanned fingerprint data across a data network 706 to afingerprint server 708. The fingerprint server 708 creates a fingerprintminutiae template of the scanned FPI and compares this template to thepreviously enrolled fingerprint minutiae templates corresponding to theindividuals cleared for access to the computer network. A match betweenthe newly created fingerprint minutiae template and one or more of thepreviously enrolled fingerprint minutiae templates indicates that theindividual is allowed access to the computer network. In addition, thefingerprint server 708 can positively identify the particular individualseeking access and, once verified, provide the identity and the relevantdata of the individual to the network server 710.

FIG. 8 depicts a block diagram of an internet logon control system 800.A fingerprint sensor or scanner 802 coupled to a user PC 804 is used toprovide a scanned fingerprint across the internet 806 to a fingerprintserver 808 that may be associated with a particular web page orassociated with a secure financial transaction that occurs over theinternet. The fingerprint server 808 creates a fingerprint minutiaetemplate of the scanned FPI and compares this template to the previouslyenrolled fingerprint minutiae templates corresponding to the individualscleared for access to the computer network. A match between the newlycreated fingerprint minutiae template and the previously enrolledfingerprint minutiae templates indicates that the individual is allowedaccess to the associated web page or that the financial transaction isproperly authorized. In addition, the fingerprint server 808 canpositively identify the particular individual seeking access, and onceverified, provide the identity of the individual to the applicationservers 810 along with personal data associated with the particularindividual.

FIG. 9 depicts a flow chart for a method of comparing a fingerprintminutiae template with a fingerprint minutiae template previously storedon a smartcard. A smartcard can be used both at a point of servicetransaction location or across a network such as the internet topositively identify the individual that is authorized to use the smartcard. An FPI is obtained from a fingerprint sensor or scanner, asdepicted in step 902. The FPI is processed, as in step 904, and if theimage is of sufficient quality, as depicted in step 906 and sufficientminutiae are identified as depicted in step 908. The FPI is analyzed andprocessed as described above according to FIG. 2 and the minutiae areextracted from the FPI and a fingerprint minutiae template is created,as depicted in step 910. Otherwise, a new FPI is obtained and control ispassed to step 902. The extracted minutiae and the fingerprint minutiaetemplate formed from the acquired FPI are compared to the fingerprintminutiae template stored on the smartcard, as depicted in step 912. If amatch occurs, as depicted in step 914, the identity of the smartcardholder is verified, as depicted in step 916, otherwise control is passedto step 902, and a new FPI is obtained.

The verification and identification functions described herein are basedon the premise that a finger being presented and scanned by thefingerprint scanner is a live finger and not a prosthetic or severedfinger having a false fingerprint. FIG. 10 depicts a flow chart for alive finger detection method that may be used in conjunction with theidentification and verification methods described herein. The binaryfingerprint image of step 214 in FIG. 2 is further analyzed to detectthe presence and size of sweat pores contained within the fingerprintimage. The binary image is provided, as depicted in step 1002. Theboundaries of the binary image are traced and chain coded, as depictedin step 1004. All clockwise closed chains are detected, as depicted instep 1006, and the area and arc length of the detected closed chains aremeasured as depicted in step 1008. Although clockwise closed chains areused to identify sweat pores, counter-clockwise closed chains can alsobe used. The measured area is compared to a sweat pore threshold and ifgreater than the sweat pore threshold, the closed chain is a detectedsweat pore. If the sweat pore exceeds a certain live finger sweat porethreshold, the finger is flagged as live, as depicted in step 1010. Inthe illustrated embodiment in which the fingerprint sensor/scanner has a500 dpi resolution, the sweat pore threshold is four pixels. Otherwise,the finger is flagged as non-living and no further processing isemployed and the identity of the individual is not confirmed. If thefinger is living and the measured arc length is compared to a holethreshold and if less than the hole threshold, the chain is removed, asdepicted in step 1012. In this manner, arcs having a arc length lessthan the hole threshold are considered to be noise and are thereforeremoved.

In some circumstances, it may be desirable to classify the FPI accordingto the location of the cores and deltas, the estimate of the maindirection of the cores, and classifying the FPI according to variouscategories of FPI. FIG. 11 depicts a flow chart of a method ofidentifying the location of the cores and deltas, estimating thedirections, and classifying the FPI. The orientation field correspondingto an FPI from step 210 of FIG. 2 is provided, as depicted in step 1102.The orientation field is refined, as depicted in step 1104 bysubdividing each block into a four sub-blocks, as depicted in step 1106.The orientation of each sub-block is predicted, using the originalorientation direction as the predictor, as depicted in step 1106. Anoctagonal core mask is created that is a vector valued 2-dimensionalmatrix having as a value a unit vector radial from the center of thecorresponding sub-block, as depicted in step 1108. The center of thecore mask is aligned with the corresponding sub-block and is convolvedwith the sub-blocks in the FPI, as depicted in step 1110.

The convolution result of the core mask and the sub-blocks isnormalized, as depicted in step 1112, and core and delta regions areidentified as having large convolution results, i.e. the singularitiesof the FPI, as depicted in step 1114. The Poincare index is determinedfor all areas of the FPI having a convolution result greater than apredetermined curve threshold, as depicted in step 1116. The Poincareindex is found by surrounding each area by a closed curve and adirection integration is performed. If the direction integration equalszero, as depicted in step 1118, the diameter of the closed curve isreduced, as depicted in step 1120, and the direction integration isperformed again. This step is repeated until the radius is one, asdepicted in step 1122, or the integration is non-zero, as depicted instep 1118.

The singularities of the FPI are classified according to the value ofthe corresponding Poincare index, as depicted in step 1116. For aPoincare index of 1, the singularities are classified as whorls and areclustered according to the corresponding Euclidean distance from thearbitrary origin. If there is more than one whorl cluster, the biggestcluster is selected and the smaller clusters are deleted. For a Poincareindex of 0.5, the singularities are cores, and are clustered accordingto the corresponding Euclidean distance from the arbitrary origin. Ifthere are more than three clusters of cores, the largest two are keptand the remaining core clusters are deleted. For a Poincare index of−0.5, the singularities are classified as deltas and are clusteredaccording to the corresponding Euclidean distance from the arbitraryorigin. If there is one whorl cluster and 3 or more delta clusters, thelargest two delta clusters are kept and the remaining delta clusters aredeleted. If there is no whorl cluster and 1 or more delta clusters, thelargest two delta clusters are kept and the remaining delta clustersdeleted.

For any cores detected in step 1116, the direction of the cores areestimated, as depicted in step 1118. The core mask from step 1106 isconvolved with the core singularity and the direction estimated from theresults in that the displacement from the core center to the mass centerof all zero sub-blocks is along the main direction of the core.

If no cores or whorl clusters are identified then cores near theboundary of the FPI are estimated. The cores near the boundary areestimated by treating as a core singularity sub-blocks near the boundaryhaving a convolution value in the top 20% of values. The cores areprocessed as described above.

The FPI is then classified as a whorl, right loop, left loop, arch, ordouble loop. An FPI having a single whorl cluster is classified as awhorl. An FPI having a core cluster, and one or less delta clusters is aloop. If the cross product of the vector from the core to the delta withthe main direction of the core is along the normal direction of thefingerprint plane, the fingerprint is a right loop. Otherwise, is if thecross product is against the normal, the fingerprint is a left loop. Ifthe cross product is nearly zero, the fingerprint is an arch. If thereare two core clusters and two or less delta clusters, the fingerprint isa double loop. If there is no core then the fingerprint is an arch.

In some circumstances, the raw fingerprint images from which one or morefingerprint minutiae templates are formed are obtained from fingerprintscanners or sensors that have different resolutions. Generally, theautomated fingerprint identification/verification process describedherein assumes that all of the raw FPIs are of the same resolution.Although this may be true for most fingerprint scanners, if the FPI hasbeen previously digitized from film, the resolution information may nothave been included with the FPI. Without a-priori knowledge of theresolution of the FPI, extra processing is required to ensure that theimages being processed are of similar resolution.

FIG. 12 depicts a method for use with the methods described herein todetermine the resolution of an FPI having an unknown resolution. The rawFPI acquired in step 1202 is divided into 16 blocks, as depicted in step1204. For each block, the Fourier transform is computed as depicted instep 1206. The magnitude of the Fourier coefficients is determined, asdepicted in step 1208. The Fourier coefficients are classified accordingto the corresponding spatial frequency, as depicted in step 1210. Theaverage magnitude of the components for each spatial frequency isdetermined, as depicted in step 1212. The spatial frequency having thelargest average magnitude is an estimation of the ridge distance of theraw FPI, as depicted in step 1214, and may be used to adjust theprocessing to allow for FPIs of similar resolution to be compared.

Those of ordinary skill in the art should further appreciate thatvariations to and modification of the above-described methods foridentifying and verifying fingerprints can be made. Accordingly, theinvention should be viewed as limited solely by the scope and spirit ofthe appended claims.

1. A method for fingerprint recognition, the method comprising the stepsof: acquiring a fingerprint image; dividing the fingerprint image intoblocks, thereby forming a blocked fingerprint image; separating theblocked fingerprint image into foreground blocks and background blocks;determining an orientation angle and amplitude for each of theforeground blocks, thereby forming an orientation field of thefingerprint; creating a ridge-enhanced image of the fingerprint;creating a binary image of the fingerprint; creating a piecewise linearimage of the fingerprint; extracting minutiae of the fingerprint fromthe piecewise linear image of the fingerprint; creating a fingerprinttemplate of the fingerprint from the extracted minutiae; and storing thefingerprint template as an enrolled fingerprint template.